Nonstationary-state hidden Markov model representation of speech signals for speech enhancement

A novel formulation of the nonstationary-state hidden Markov model (NS-HMM), employed as the speech model and serving as the theoretical basis for the construction of a speech enhancement system, is presented in this paper. The NS-HMM is used as a compact, parametric model, generalized from the stationary-state HMM, for describing clean speech statistics in the construction of the minimum mean-square-error (MMSE) speech enhancement system. The feature selection problem associated with the use of the NS-HMM in designing the speech enhancement system is addressed. The MMSE formulation is derived where the NS-HMM is used as the clean speech model and Gaussian-mixture, stationary-state HMM as the additive noise model. Speech enhancement experiments are conducted, demonstrating superiority of the NS-HMM over the stationary-state HMM in the speech enhancement performance for low SNRs. Detailed diagnostic analysis on the speech enhancement system's operation shows that the superiority arises from the ability of the NS-HMM to fit the spectral trajectory of the signal embedded in noise more closely than the stationary-state HMM.